Hyperspectral image spectral-spatial cooperative classification method based on SAE depth network

Abstract

The invention relates to a hyperspectral image spectral-spatial cooperative classification method based on an SAE depth network. According to the hyperspectral image spectral-spatial cooperative classification method based on the SAE depth network, the conventional method that neighborhood information is applied to act as spatial features after PCA dimension reduction, spatial position information, i.e. row and column coordinates, of current pixel points are applied to act as the spatial features and then the spatial features are combined with spectral features to act as the spectral-spatial cooperative features of training samples can be substituted. The beneficial effects of the hyperspectral image spectral-spatial cooperative classification method based on the SAE depth network are that the conventional method of spatial feature extraction in spectral-spatial cooperative classification based on the SAE depth network is improved, the spatial position information is applied to act as the spatial features rather than the conventional method of spatial feature extraction that principal component analysis (PCA) dimension reduction is performed in spectral space and then neighborhood space information is extracted to act as the spatial features, the extraction method of the spatial features is simplified, computational burden is reduced and classification accuracy is enhanced in comparison with that of the conventional method.

Description

technical field [0001] The invention belongs to the technical field of information processing, and relates to a method for classifying hyperspectral images, in particular to a method for classifying space-spectrum joint hyperspectral images based on an SAE deep network. Background technique [0002] Hyperspectral images have high spectral resolution, multiple imaging bands, and a large amount of information, and are being more and more widely used in the field of remote sensing. Hyperspectral image classification techniques play an important role in these applications. In the past ten years, the research work of artificial neural network has been deepened. According to the neural network, it can realize the approximation of a certain algorithm or function. People use the neural network structure to realize the classification of hyperspectral images. The more layers of the neural network, the other The ability to represent information is also stronger. [0003] Because of t...

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Problems solved by technology

[0005] In order to avoid the deficiencies of the prior art, the present invention proposes a hyperspectral image space-spectrum joint classification method based on SAE deep network, which overcomes the large amount of calculation and the large amount of calculation for extracting spatial features in the traditional space-spectral joint classification method based on SAE deep network. Complicated, the problem that the loss of spectral information affects the classification accuracy

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